Degree | Type | Year |
---|---|---|
2503873 Interactive Communication | OB | 3 |
You can view this information at the end of this document.
This course does not have any compulsory requirements, but it is recommended that students have previously passed the following courses:
Information Systems
Information Storage and Retrieval
Advanced Web Services
The main objective of the course is to introduce students to the basic concepts and main practices of Big Data.
The course also has the following specific objectives:
1. To introduce the concepts of data sources and types of data (structure, classification, integration and quality).
2. To make the first approaches to database analysis in a spreadsheet environment and other practical tools.
3. To promote the exploration of requests and work with open data sources.
4. To develop a propaedeutic knowledge for the further development of Business Intelligence applications: the development of big data solutions for business intelligence and its influence on decision making.
Unit 1. Big Data: Introduction to the subject: concept of Big Data, its processes and characteristics. Artificial Intelligence and Big Data.
Unit 2. Sources, capture and storage of data: Presentation of data sources (mainly open sources. Processes of access and requests for public information and transparency laws. Processes for searching, downloading and storing different types of data (formats).
Unit 3. Data processing and analysis: Handling of data cleaning and analysis tools and functions for decision making. Basic statistics for Big Data.
Unit 4. Social Media data analysis and monitoring: Introduction to Social Media as a source of big data: presentation of techniques and tools to extract insights from the social networks.
Unit 5. Data visualization and data mapping: Presentation of different tools and possibilities of data visualization and cartographic representation of information for decision-oriented reporting.
(*) The detailed calendar with the content of the different sessions will be displayed on the day of the presentation of the course. It will also be posted on the Virtual Campus where students will be able to find a detailed description of the exercises and practices, the various teaching materials and any information necessary for the proper monitoring of the course. In the event of a change of teaching modality for health reasons, the teaching staff will inform of the changes that will take place in the course programme and in the teaching methodologies.
The content of this course will be sensitive to aspects related to the gender perspective.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Laboratory | 33 | 1.32 | 1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23 |
Theoretical sessions | 15 | 0.6 | 2, 3, 6, 9, 10, 11, 18, 21 |
Type: Supervised | |||
Mentoring | 10 | 0.4 | 7, 15, 16, 19, 20, 21, 22 |
Seminars | 10 | 0.4 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 |
Type: Autonomous | |||
Autonomous work: reading and coursework preparation and personal study | 60 | 2.4 | 3, 4, 5, 6, 7, 9, 11, 12, 15, 16, 18, 19, 20, 21, 22 |
The structure of the course, in which different practical activities are carried out, seeks to internalise skills related to the management of Big Data (search, extraction, analysis and publication of data for decision-making). Its methodology is completely practical. Through laboratory activities, workshops and the final project, both the theoretical component of the subject and the practical application of the contents studied are evaluated.
The continuous assessment of the course, in which specific and continuous short-term practical activities are carried out, allows for a very precise monitoring of the student's learning and progression. In addition, activities are done progressively on the acquisition of knowledge that, step by step, is involved in the next activities.
The Introduction to Big Data course includes three types or categories of assessable training activities:
Laboratory exercises: individual or team work in which practical activities are carried out with a punctual deliverable with a time limit. Students must apply the knowledge, distribute the time and prepare the deliverables within the classroom and in the hours set aside for practice under the guidance of the professor.
Seminars: individual or team work involving more extensive practical activities with deliverables open to students' creativity. There are no time limits in the classroom, but there are deadlines. Students must apply knowledge, allocate time and prepare deliverables by starting their work in the classroom, but continuing it in the form of activities supervised by the teaching team.
Development of the final course work: practical group evaluation exercise in which students must solve, during the course, a practical application problem related to Big Data. Students must state the problem and carry out the four processes to provide a proposed solution based on large amounts of data: search, extraction, analysis and publication of a data report that includes a proposed decision based on the information collected and analysed.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Classroom exercises | 30% | 8 | 0.32 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 |
Courseworks | 30% | 6 | 0.24 | 1, 2, 3, 4, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 |
Laboratory | 40% | 8 | 0.32 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 |
Continuous evaluation
The assessment activities are as follows:
Activity A: Laboratory exercises, which have a weight of 40% of the final qualification.
Activity B: Classroom exercises, with a weight of 30% of the final qualification.
Activity C: Courseworks, which weighs 30% of the final grade.
In order to pass the course, a minimum pass mark (5.0) must be obtained in each of the activities.
REVALUATION:
In the last three weeks of the course, students who have not passed the course may sit a revaluation test consisting of a theoretical test and a practical exercise. The compulsory condition to be eligible for the revaluation of the course is to have done at least 2/3 of the total number of practical exercises of the course (activities A, B and C) and to have obtained an average mark equal to or higher than 3.5 (and lower than 5) in all the assessment activities.
In accordance with the above criteria, if a student does not complete at least 66% of the practical activities of the assessment activities, he/she will be considered as not assessable in this course.
SECOND ENROLLMENT:
In the case of second enrollment, students may take a single synthesis test that will consist of a theoretical test and a practical exercise. The qualification of the subject will correspond to the qualification of the synthesis test. The student who wants to take this synthesis test must notify the coordinator of the subject.
PLAGIARISM:
In the event that the student performs any irregularity that may lead to a significant variation of an evaluation act, this evaluation act will be graded with 0, regardless of the disciplinary process that could be instructed. In the event, that several irregularities occur in the evaluation acts of the same subject, the final grade for this subject will be 0.
Thissubject doesn't provide for the single assessment system.
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As this is a completely practical course, the software required is the usual one for the tasks of capturing, processing and analysing information in different formats.
Specifically, the following tools are required:
Text editing software: Word or similar
Data analysis software: Excel or similar
Data visualisation software: Flourish - Datawrapper - Gephi
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(PLAB) Practical laboratories | 61 | Spanish | second semester | afternoon |
(PLAB) Practical laboratories | 62 | Spanish | second semester | afternoon |
(TE) Theory | 6 | Spanish | second semester | afternoon |